{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1 引言\n",
    "集成学习算法是当下炙手可热的一类算法，在诸多机器学习大赛中都频繁出现它的身影。准确来说，集成学习算法并不是一个单独的机器学习算法，而是通过构建多个学习器，博采众家之长，共同求解问题的一种思想。古语有云：“三个臭皮匠顶个诸葛亮”。一个简单的学习器也许不能很好的拟合数据，但是结合多个不同的学习器去解决问题，往往就可能有更加不俗的表现。\n",
    "本篇博文中，我们先来详细说说集成学习思想以及分类，然后对其中的Bagging算法展开介绍。\n",
    "\n",
    "# 2 集成学习\n",
    "引言中说过，所谓集成学习就是先产生一组单个的学习器，我们姑且将这些单个的学习器称为“个体学习器”，然后通过某种策略将这些个体学习器结合起来共同完成学习任务，如下图所示。那么，有两个问题需要解决：第一，如果获得个体学习器；第二，如何将各个体学习器的结果结合到一起。  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image]()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在回答第一个问题前，我们必须明确，对集成学习中单个学习器必须满足两个条件：  \n",
    "（1）学习器之间应该有差异性。如果使用的单个学习器将没有差异，那么集成起来的结果是没有变化的。  \n",
    "（2）每个学习器的精度必须大于0.5。在集成学习中，单个分类器不需要很强，因为越强的分类器训练过程就越复杂，甚至容易发生过拟合，只需要保证每个学习器准确率大于0.5，因为如果单个学习的的准确率小于0.5，随着集成规模的增加，分类精度不断下降，反之如果精度大于0.5，就算简单的学习器，通过足够数量的组合最终精度也会可以趋向于1。可以通过下图来理解这两个条件。\n",
    "![image]()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在这两个条件前提下，对于第一个问题，有两种解决思路。一种是使用不同类别的算法来构建个体学习器，例如对于同一个任务分别使用决策树算法、支持向量机、神经网络等不同算法来构建学习器。另一种思路是所有个体学习器都使用同一种算法进行构建，这种思路是目前集成学习算法的主流。在所有个体学习器都使用同种算法构建时，如何保证学习器之间的差异性呢？有两种方案：\n",
    "- 每次训练个体学习器时，对原始数据集进行抽样获得不同数据集作为当前训练集，每一个训练样本在抽样训练集中可以多次或不出现，经过$T$次训练后，可得到$T$个不同的没有相互依赖的个体学习器学习器。Bagging、随机森林就是这种方案的代表。\n",
    "- 通过更新权重的方式不断地使用一个弱学习器弥补前一个弱学习器的“不足”的过程，来串行地构造一个较强的学习器，这个强学习器能够使目标函数值足够小。这一方案的代表是Boosting系列的算法，包括Adaboost、GBDT、XGBOOST等\n",
    "\n",
    "在本文中，我们先对第一种方案的两种算法——Bagging和随机森林进行介绍，在后续的博文中，再对Adaboost、GBDT等算法进行分析。\n",
    "\n",
    "# 3 Bagging\n",
    "Bagging是并行式集成学习方法的最典型代表，算法名称来源于Bootstrap aggregating的简写，又称装袋算法，这种算法直接采用自助采样法获得$T$个各不相同的数据集，分别使用这$T$个数据集进行训练可获得$T$个个体学习器，再将这些学习器组合起来共同完成分类或者回归任务。当完成分类任务时，采用简单投票法对$T$个体学习器结果进行组合后输出；当染成回归任务时，采用简单平均法对$T$个个体学习器学习结果进行组合输出。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image]()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.1 自助采样法\n",
    "自助采样法（Bootstrap sampling是一种从给定原始数据集中有放回的均匀抽样，也就是说，每当选中一个样本，它等可能地被再次选中并被再次添加到训练集中。对于给定包含$m$个样本的原始数据集$D$，进行自助采样获得$D'$，具体操作方式：每次采样时，从几何$D$中随机抽取一个样本拷贝一份到集合$D'$中，然后将样本放回集合$D$中，是的该羊被后续采样中仍有可能被采集到；重复这一步骤$m$次后，就可以获得同样包含$m$个样本的集合$D'$，集合$D'$就是自助采样的最终结果。可以想象，集合$D$中的样本有一部分会在集合$D'$中出现重复出现，而有些样本却一次都不出现。在$m$次抽样中，某个样本从未被抽到的概率为${(1 - \\frac{1}{m})^m}$，当集合$D$样本足够多时有：\n",
    "$$\\mathop {\\lim }\\limits_{m \\to \\infty } {(1 - \\frac{1}{m})^m} = \\frac{1}{e} \\approx 0.368$$\n",
    "也就是说，原始集合$D$中有36.8%的样本不包含在通过自助采样法获得的集合$D'$中。在Bagging中，未被采集到的36.8%的样本可以用作测试集对个体学习器性能进行评估，当个体学习器使用决策树算法构建时，这部分用本可以用来辅助树剪枝；使用神经网络构建个体学习器时，可以用来防止过拟合。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.2 结合策略\n",
    "假设共有$T$个个体学习器，以$\\{ {h_1},{h_2}, \\cdots ,{h_T}\\} $表示，其中样本$x$经$h_i$后的输出值为$h_i(x)$。对于结合$T$个个体学习器输出值，主要有一下几种策略：\n",
    "\n",
    "**（1）平均法**\n",
    "平均法常用于回归类任务的数值型输出，包括简单平均法、加权平均法等。\n",
    "- **简单平均法**\n",
    "$$H(x) = \\frac{1}{T}\\sum\\limits_{i = 1}^T {{h_i}(x)} $$\n",
    "- **加权平均法**\n",
    "$$H(x) = \\sum\\limits_{i = 1}^T {{w_i}{h_i}(x)} $$\n",
    "式中，$w_i$是个体学习器$h_i$的权重，通常要求${w_i} \\geqslant 0$且$\\sum\\limits_{i = 1}^T {{w_i}}  = 1$。至于$w_i$的具体值，可以根据$h_i$的具体表现来确定，$h_i$准确率越高，$w_i$越大。  \n",
    "对于两种平均法的选择上，当个体学习器性能相差较大时，选用加权平均法；当各个体学习器性能相近时，使用简单加权平均法。\n",
    "\n",
    "\n",
    "**（2）投票法**  \n",
    "投票法更多用于作为分类任务的集成学习的结合策略。\n",
    "- **相对多数投票法**\n",
    "也可以认为是多数决策法，即预测结果中票数最高的分类类别。如果不止一个类别获得最高票，则随机选择一个作为最终类别。\n",
    "- **绝对多数投票法**\n",
    "不光要求获得票数最高，而且要求票数过半，否则决绝输出。\n",
    "- **加权投票法**\n",
    "与加权平均法类似，每个个体学习器的分类票数要乘以一个权重，最终将各个类别的加权票数求和，最大的值对应的类别为最终类别。\n",
    "\n",
    "\n",
    "**（3）学习法**  \n",
    "学习法是一种比平均法和投票法更为强大复杂的结合策略，学习法以所有个体学习器的输出作为一个数据集，额外使用一个学习器对该数据及进行学习，然后输出最终的结果。Stacking方法是学习法的一个经典代表，目前大多数应用中所说的学习法都是指Stacking方法。甚至因为Stacking方法的特殊性和复杂性，很多资料中将Stacking方法当做是与Bagging和Boosting一样的一类集成学习算法。  \n",
    "Stacking方法中将之前提到的所有个体学习器称为初级学习器，将用于结合的学习器称为次级学习器。Stacking方法先从原始数据集训练处初级学习器，然后“生成”一个新的数据集用于训练次级学习器。在新的数据集中，初级学习器的输出被当做样本输出特征，而初始样本的类别标签人被当做新数据及的类别标签。（注：关于Stacking可以参考[这篇博客](https://www.cnblogs.com/wqbin/p/11634111.html)）\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4 随机森林\n",
    "\n",
    "\n",
    "## 4.1 算法介绍"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "随机森林（Random Forest，建成RF）也是一种十分流行的算法，原理与Bagging非常相似，甚至有很多资料认为随机森林是Bagging的一个分支，一个扩展变体。如果已经理解了Bagging算法，那么现在再来看随机森林将再将单不过。  \n",
    "从名称上可以推测，随机森林是以决策树为学习算法构建个体学习器并采用Bagging思想集成的一种算法。确实也是如此，但却不止如此，因为随机森林在构建决策时，不仅在样本选择上进行了随机采样，同时在特征属性的选择上也进行了随机选取。在之前介绍决策树算法的博客中说过，传统的决策树算法在选择最优特征属性时总是从当前数据集所有特征属性（假设共有$d$个特征属性）中选择一个最优的特征属性作为当前树节点对数据集进行划分；但在随机森林中，使用决策树算法构建个体学习器时，先从$d$个属性中随机选择$k$个组成新的训练集，选择最优分裂属性时，从这$k$个属性中进行择优选取。这就是随机森林中构建决策树与传统决策树的不同。  \n",
    "\n",
    "需要注意，有两个因素对随机森林性能影响很大：\n",
    "- 森林中任意两棵树的相关性：相关性越大，错误率越大；\n",
    "- 森林中每棵树的分类能力：每棵树的分类能力越强，整个森林的错误率越低  \n",
    "\n",
    "$k$控制了选择特征训练集的随机程度，无论是相关性还是分类能力，都与$k$值选取息息相关，减小特征选择个数$k$，树的相关性和分类能力也会相应的降低；增大$k$，两者也会随之增大。所以关键问题是如何选择最优的m（或者是范围），这也是随机森林唯一的一个参数。当$k=d$时，与传统的决策树算法就没有什么区别了，都是从原始完整的训练集中进行选择，当$k=1$时，则是随机选择一个特征属性进行训练；一般情况下，推荐$k = {\\log _2}d$。  \n",
    "\n",
    "随机森林不仅在每个个体学习器训练样本选择上，延用了Bagging算法中的自助采样法，保证了每个个体学习器训练集的差异性，同时也通过特征属性的选择，进一步进行扰动，保证了个体信息器的多样性，这也是随机森林在众多集成算法中表现突出的原因。  最后总结一下随机森林的优缺点：  \n",
    "\n",
    "优点：  \n",
    "（1） 每棵树都选择部分样本及部分特征，一定程度避免过拟合；  \n",
    "（2） 每棵树随机选择样本并随机选择特征，使得具有很好的抗噪能力，性能稳定；  \n",
    "（3） 能处理很高维度的数据，并且不用做特征选择；  \n",
    "（4） 适合并行计算；  \n",
    "（5） 实现比较简单；  \n",
    "缺点：  \n",
    "（1）当随机森林中的决策树个数很多时，训练时需要的空间和时间会较大；  \n",
    "（2）随机森林模型还有许多不好解释的地方，有点算个黑盒模型。  \n",
    "\n",
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.2 代码实现"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "为了方便展示，还是使用自定义的二维数据集："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt \n",
    "import copy\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "\n",
    "\n",
    "a = np.random.normal(20,5,300)\n",
    "b = np.random.normal(15,5,300)\n",
    "c = np.random.normal(20,5,300)\n",
    "cluster1 = np.array([[x, y, z, 1] for x, y, z in zip(a,b, c)])\n",
    "\n",
    "a = np.random.normal(20,5,300)\n",
    "b = np.random.normal(45,5,300)\n",
    "c = np.random.normal(45,5,300)\n",
    "cluster2 = np.array([[x, y, z, 2] for x, y, z in zip(a,b,c)])\n",
    "\n",
    "a = np.random.normal(55,5,300)\n",
    "b = np.random.normal(30,5,300)\n",
    "c = np.random.normal(45,5,300)\n",
    "cluster3 = np.array([[x, y, z, 3] for x, y, z in zip(a,b,c)])\n",
    "\n",
    "dataset = np.append(np.append(cluster1,cluster2, axis=0),cluster3, axis=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.utils import shuffle\n",
    "x_train,x_test,y_train,y_test = train_test_split(dataset[:,:3],dataset[:,-1],test_size=0.3,random_state=0)  # 将数据划分为训练集，测试集\n",
    "x_train,y_train = shuffle(x_train,y_train)  # 随机打乱数据\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "ax.set_zlabel('Z')  # 坐标轴\n",
    "ax.set_ylabel('Y')\n",
    "ax.set_xlabel('X')\n",
    "ax.scatter(x_train[:,0], x_train[:,1], x_train[:,2])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/chb/anaconda3/envs/study_python/lib/python3.7/site-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "                       max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators=10,\n",
       "                       n_jobs=None, oob_score=False, random_state=None,\n",
       "                       verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier   # 导入随机森林\n",
    "clf = RandomForestClassifier()\n",
    "clf.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([31.35045639, 14.1587136 , 11.3989477 ,  1.        ])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.predict([[22.63809831, 24.57126294, 18.54161034]])  # 对当个样本类别进行预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "模型准确率为： 1.0\n"
     ]
    }
   ],
   "source": [
    "# 验证准确率\n",
    "from sklearn.metrics import accuracy_score\n",
    "print('模型准确率为：',accuracy_score(y_test, clf.predict(x_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "for x, y, z, p in zip(x_test[:,0], x_test[:,1], x_test[:,2], y_test):\n",
    "    if int(p)==1:\n",
    "        ax.scatter(x, y, z, c='r')\n",
    "    elif int(p)==2:\n",
    "        ax.scatter(x, y, z, c='y')\n",
    "    else:\n",
    "        ax.scatter(x, y, z, c='g')\n",
    "ax.set_zlabel('Z')  # 坐标轴\n",
    "ax.set_ylabel('Y')\n",
    "ax.set_xlabel('X')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "study_python",
   "language": "python",
   "name": "study_python"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
